Property partner is a property crowdfunding site…
Data source: open house (July 2017).
pp_resale <- pp_resale %>%
mutate(`Transaction (£)` = `Unit Count` * `Unit Price`) %>%
mutate(Year = year(`Last Transaction`),
Month = month(`Last Transaction`),
Week = week(`Last Transaction`),
Day = day(`Last Transaction`)) %>%
mutate(`Transaction size (£)` = case_when(`Transaction (£)` < 100 ~ "£0-£99",
`Transaction (£)` < 500 ~ "£100-£499",
`Transaction (£)` < 5000 ~ "£500-£5,000",
`Transaction (£)` < 50000 ~ "£5,000-£50,000",
`Transaction (£)` >= 50000 ~ "£50,000+") %>%
factor(levels = c("£0-£99", "£100-£499", "£500-£5,000", "£5,000-£50,000", "50,000+")))
plot_resales_over_time <- pp_resale %>%
mutate(`Last Transaction` = floor_date(`Last Transaction`, unit = "month")) %>%
group_by(`Last Transaction`, `Transaction type`) %>%
summarise(`Transaction (£)` = sum(`Transaction (£)`)) %>%
ggplot(aes(x = `Last Transaction`, y = `Transaction (£)`, fill = `Transaction type`)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels = comma) +
labs(title = "Property Partner Transactions") +
theme_minimal()
ggplotly(plot_resales_over_time)
plot_resales_over_time_by_size <- pp_resale %>%
mutate(`Last Transaction` = floor_date(`Last Transaction`, unit = "month")) %>%
group_by(`Last Transaction`, `Transaction size (£)`) %>%
summarise(`Transaction (£)` = sum(`Transaction (£)`)) %>%
ggplot(aes(x = `Last Transaction`, y = `Transaction (£)`, fill = `Transaction size (£)`)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels = comma) +
labs(title = "Property Partner Transactions; by Transaction size (£)",
caption = "Facetted by Transaction size (£), aggregated by month") +
theme_minimal() +
facet_wrap(~`Transaction size (£)`)
ggplotly(plot_resales_over_time_by_size)
pp_resale %>%
ggplot(aes(x = `Transaction (£)`, y = factor(Year), fill = `Transaction size (£)`)) +
geom_joy() +
facet_wrap(~`Transaction size (£)`, scales = "free_x") +
ylab("Year") +
labs(title = "Distribution of transactions",
subtitle = "Facetted by Transaction size (£), over time") +
theme_minimal() +
theme(legend.position = "none")
## Picking joint bandwidth of 4.49
## Picking joint bandwidth of 17.7
## Picking joint bandwidth of 82.7
## Picking joint bandwidth of 978
pp_resale %>%
ggplot(aes(x = factor(Year), y = `Transaction (£)`, fill = `Transaction size (£)`, colour = `Transaction size (£)`)) +
geom_violin(draw_quantiles = c(0.25, 0.5, 0.75), alpha = 0.2) +
geom_jitter(width = 0.3, alpha = 0.01, data = sample_n(pp_resale, size = round(nrow(pp_resale)/10, digits = 0))) +
facet_wrap(~`Transaction size (£)`, scales = "free") +
xlab("Year") +
labs(title = "Distribution of transactions",
subtitle = "Facetted by Transaction size (£), over time",
caption = "Plotted points represent a 10% sample of the data") +
theme_minimal() +
theme(legend.position = "none")